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Histogram of oriented gradients

About: Histogram of oriented gradients is a research topic. Over the lifetime, 2037 publications have been published within this topic receiving 55881 citations. The topic is also known as: HOG.


Papers
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Proceedings ArticleDOI
28 May 2012
TL;DR: A classifier which was trained through histogram of oriented gradients features to judge the likelihood of candidate plates detected by Haar classifier, and selected the candidate with highest likelihood as the final plate, in order to reduce the false positives.
Abstract: The Haar-like cascaded classifier has been used in license plate detection and yields a high detection rate, but it often has high false positives. We introduced a classifier which was trained through histogram of oriented gradients (HOG) features to judge the likelihood of candidate plates detected by Haar classifier, and selected the candidate with highest likelihood as the final plate, in order to reduce the false positives. This method was tested on 3000 images to obtain a recall rate of 95.2%, and accuracy of 94.0% as opposed to 66.4% without using HOG features. It was shown that the proposed method is able to eliminate most of the false candidate plates, such as barriers and incomplete plates.

32 citations

Proceedings ArticleDOI
01 Sep 2016
TL;DR: This work proposes a novel multi-class image database for hair detection in the wild, called Figaro, which tackles the problem of hair detection without relying on a-priori information related to head shape and location.
Abstract: Hair is one of the elements that mostly characterize people appearance. Being able to detect hair in images can be useful in many applications, such as face recognition, gender classification, and video surveillance. To this purpose we propose a novel multi-class image database for hair detection in the wild, called Figaro. We tackle the problem of hair detection without relying on a-priori information related to head shape and location. Without using any human-body part classifier, we first classify image patches into hair vs. non-hair by relying on Histogram of Gradients (HOG) and Linear Ternary Pattern (LTP) texture features in a random forest scheme. Then we obtain results at pixel level by refining classified patches by a graph-based multiple segmentation method. Achieved segmentation accuracy (85%) is comparable to state-of-the-art on less challenging databases.

31 citations

Journal ArticleDOI
TL;DR: An efficient method by combining the two most successful local feature descriptors such as Pyramid Histogram of Oriented Gradients and Local Directional Patterns to represent ear images to achieve promising recognition performance in comparison with other existing successful methods is presented.
Abstract: Achieving higher recognition performance in uncontrolled scenarios is a key issue for ear biometric systems. It is almost difficult to generate all discriminative features by using a single feature extraction method. This paper presents an efficient method by combining the two most successful local feature descriptors such as Pyramid Histogram of Oriented Gradients (PHOG) and Local Directional Patterns (LDP) to represent ear images. The PHOG represents spatial shape information and the LDP efficiently encodes local texture information. As the feature sets are curse of high dimension, we used principal component analysis (PCA) to reduce the dimension prior to normalization and fusion. Then, two normalized heterogeneous feature sets are combined to produce single feature vector. Finally, the Kernel Discriminant Analysis (KDA) method is employed to extract nonlinear discriminant features for efficient recognition using a nearest neighbor (NN) classifier. Experiments on three standard datasets IIT Delhi version (I and II) and University of Notre Dame collection E reveal that the proposed method can achieve promising recognition performance in comparison with other existing successful methods.

31 citations

Journal ArticleDOI
TL;DR: A computer vision based system for fast robust Traffic Sign Detection and Recognition (TSDR), consisting of three steps, which compares four features descriptors which include Histogram of Oriented Gradients (HOG), Gabor, Local Binary Pattern (LBP), and Local Self-Similarity (LSS).
Abstract: In this paper, we present a computer vision based system for fast robust Traffic Sign Detection and Recognition (TSDR), consisting of three steps. The first step consists on image enhancement and thresholding using the three components of the Hue Saturation and Value (HSV) space. Then we refer to distance to border feature and Random Forests classifier to detect circular, triangular and rectangular shapes on the segmented images. The last step consists on identifying the information included in the detected traffic signs. We compare four features descriptors which include Histogram of Oriented Gradients (HOG), Gabor, Local Binary Pattern (LBP), and Local Self-Similarity (LSS). We also compare their different combinations. For the classifiers we have carried out a comparison between Random Forests and Support Vector Machines (SVMs). The best results are given by the combination HOG with LSS together with the Random Forest classifier. The proposed method has been tested on the Swedish Traffic Signs Data set and gives satisfactory results.

31 citations

Journal ArticleDOI
TL;DR: An accurate and effective event detection method to detect events from a Twitter stream, which uses visual and textual information to improve the performance of the mining process, is developed.
Abstract: In this contribution, we develop an accurate and effective event detection method to detect events from a Twitter stream, which uses visual and textual information to improve the performance of the mining process. The method monitors a Twitter stream to pick up tweets having texts and images and stores them into a database. This is followed by applying a mining algorithm to detect an event. The procedure starts with detecting events based on text only by using the feature of the bag-of-words which is calculated using the term frequency-inverse document frequency (TF-IDF) method. Then it detects the event based on image only by using visual features including histogram of oriented gradients (HOG) descriptors, grey-level cooccurrence matrix (GLCM), and color histogram. K nearest neighbours (Knn) classification is used in the detection. The final decision of the event detection is made based on the reliabilities of text only detection and image only detection. The experiment result showed that the proposed method achieved high accuracy of 0.94, comparing with 0.89 with texts only, and 0.86 with images only.

31 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202356
2022181
2021116
2020189
2019179
2018240